[Model] Add smolvlm support (#16017)

Signed-off-by: chaunceyjiang <chaunceyjiang@gmail.com>

docs/source/models/supported_models.md

@@ -990,6 +990,13 @@ See [this page](#generative-models) for more information on how to use generativ
   *
   * ✅︎
   * ✅︎
+- * `SmolVLMForConditionalGeneration`
+  * SmolVLM2
+  * T + I
+  * `SmolVLM2-2.2B-Instruct`
+  *
+  * ✅︎
+  * ✅︎
 - * `UltravoxModel`
   * Ultravox
   * T + A<sup>E+</sup>

examples/offline_inference/vision_language.py

@@ -298,6 +298,34 @@ def run_idefics3(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+# SmolVLM2-2.2B-Instruct
+def run_smolvlm(questions: list[str], modality: str) -> ModelRequestData:
+    assert modality == "image"
+    model_name = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=8192,
+        max_num_seqs=2,
+        enforce_eager=True,
+        mm_processor_kwargs={
+            "max_image_size": {
+                "longest_edge": 384
+            },
+        },
+        disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
+    )
+    prompts = [
+        (f"<|im_start|>User:<image>{question}<end_of_utterance>\nAssistant:")
+        for question in questions
+    ]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # InternVL
 def run_internvl(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -955,6 +983,7 @@ model_example_map = {
     "qwen2_vl": run_qwen2_vl,
     "qwen2_5_vl": run_qwen2_5_vl,
     "skywork_chat": run_skyworkr1v,
+    "smolvlm": run_smolvlm,
 }
 
 

examples/offline_inference/vision_language_multi_image.py

@@ -217,6 +217,33 @@ def load_idefics3(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
+def load_smolvlm(question: str, image_urls: list[str]) -> ModelRequestData:
+    model_name = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
+
+    # The configuration below has been confirmed to launch on a single L40 GPU.
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=8192,
+        max_num_seqs=16,
+        enforce_eager=True,
+        limit_mm_per_prompt={"image": len(image_urls)},
+        mm_processor_kwargs={
+            "max_image_size": {
+                "longest_edge": 384
+            },
+        },
+    )
+
+    placeholders = "\n".join(f"Image-{i}: <image>\n"
+                             for i, _ in enumerate(image_urls, start=1))
+    prompt = f"<|im_start|>User:{placeholders}\n{question}<end_of_utterance>\nAssistant:"  # noqa: E501
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        image_data=[fetch_image(url) for url in image_urls],
+    )
+
+
 def load_internvl(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "OpenGVLab/InternVL2-2B"
 
@@ -614,6 +641,7 @@ model_example_map = {
     "qwen_vl_chat": load_qwen_vl_chat,
     "qwen2_vl": load_qwen2_vl,
     "qwen2_5_vl": load_qwen2_5_vl,
+    "smolvlm": load_smolvlm,
 }
 
 

requirements/test.in

@@ -28,6 +28,7 @@ torchvision==0.21.0
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
 mistral_common[opencv] >= 1.5.4 # required for pixtral test
+num2words # required for smolvlm test
 opencv-python-headless >= 4.11.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]==0.4.8 # required for model evaluation test

requirements/test.txt

@@ -101,6 +101,8 @@ dill==0.3.8
     #   multiprocess
 dnspython==2.7.0
     # via email-validator
+docopt==0.6.2
+    # via num2words
 docutils==0.16
     # via awscli
 einops==0.8.0
@@ -263,6 +265,8 @@ networkx==3.2.1
     # via torch
 nltk==3.9.1
     # via rouge-score
+num2words==0.5.14
+    # via -r requirements/test.in
 numba==0.61.0
     # via
     #   -r requirements/test.in

tests/models/decoder_only/vision_language/test_models.py

@@ -493,6 +493,16 @@ VLM_TEST_SETTINGS = {
         patch_hf_runner=model_utils.skyworkr1v_patch_hf_runner,
         marks=[large_gpu_mark(min_gb=80)],
     ),
+    "smolvlm": VLMTestInfo(
+        models=["HuggingFaceTB/SmolVLM2-2.2B-Instruct"],
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
+        prompt_formatter=lambda img_prompt:f"<|im_start|>User:{img_prompt}<end_of_utterance>\nAssistant:",  # noqa: E501
+        img_idx_to_prompt=lambda idx: "<image>",
+        max_model_len=8192,
+        max_num_seqs=2,
+        auto_cls=AutoModelForImageTextToText,
+        hf_output_post_proc=model_utils.smolvlm_trunc_hf_output,
+    ),
     ### Tensor parallel / multi-gpu broadcast tests
     "chameleon-broadcast": VLMTestInfo(
         models=["facebook/chameleon-7b"],

tests/models/decoder_only/vision_language/vlm_utils/model_utils.py

@@ -204,6 +204,12 @@ def idefics3_trunc_hf_output(hf_output: RunnerOutput,
     return output_ids, output_str, out_logprobs
 
 
+def smolvlm_trunc_hf_output(hf_output: RunnerOutput,
+                            model: str) -> RunnerOutput:
+    # Based on Idefics3
+    return idefics3_trunc_hf_output(hf_output, model)
+
+
 def minicpmv_trunc_hf_output(hf_output: RunnerOutput,
                              model: str) -> RunnerOutput:
     output_ids, output_str, out_logprobs = hf_output

tests/models/multimodal/processing/test_common.py

@@ -257,6 +257,7 @@ def _test_processing_correctness_mistral(
     "h2oai/h2ovl-mississippi-800m",
     "OpenGVLab/InternVL2-1B",
     "HuggingFaceM4/Idefics3-8B-Llama3",
+    "HuggingFaceTB/SmolVLM2-2.2B-Instruct",
     "meta-llama/Llama-4-Scout-17B-16E-Instruct",
     "llava-hf/llava-1.5-7b-hf",
     "llava-hf/llava-v1.6-mistral-7b-hf",

tests/models/multimodal/processing/test_smolvlm.py

+# SPDX-License-Identifier: Apache-2.0
+"""Tests for smolvlm's multimodal preprocessing kwargs."""
+import pytest
+from transformers import SmolVLMConfig
+
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+from ....conftest import _ImageAssets
+from ...utils import build_model_context
+
+
+@pytest.mark.parametrize("model_id", ["HuggingFaceTB/SmolVLM2-2.2B-Instruct"])
+# yapf: disable
+@pytest.mark.parametrize(
+    ("mm_processor_kwargs", "expected_toks_per_img"),
+    [
+        ({"max_image_size": {"longest_edge": 384}}, 1377),
+        ({"max_image_size": {"longest_edge": 768}}, 405),
+    ])
+# yapf: enable
+@pytest.mark.parametrize("num_imgs", [1, 2])
+@pytest.mark.parametrize("kwargs_on_init", [True, False])
+def test_processor_override(
+    image_assets: _ImageAssets,
+    model_id: str,
+    mm_processor_kwargs: dict[str, object],
+    expected_toks_per_img: int,
+    num_imgs: int,
+    kwargs_on_init: bool,
+):
+    """Ensure Idefics3MultiModalProcessor handles num_crops properly."""
+    # Same as the previous test - don't initialize mm_processor_kwargs
+    # in this test and assume that the kwargs will be correctly expanded by
+    # the partial when calling the custom input processor.
+    ctx = build_model_context(
+        model_id,
+        mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
+        limit_mm_per_prompt={"image": num_imgs},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+    hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
+
+    # Build the image str / prompt based on the number of images we pass
+    placeholders = "<image>" if num_imgs == 1 else "\n".join(
+        f"Image-{i}: <image>\n" for i in range(1, num_imgs + 1))
+    prompt = f"<|im_start|>User:{placeholders}\n<end_of_utterance>\nAssistant:"  # noqa: E501
+
+    # Build mm_data
+    image_size = ctx.get_hf_config(SmolVLMConfig).vision_config.image_size
+    dummy_image_size = (image_size * 4, image_size * 4)
+    dummy_image = image_assets[0].pil_image.resize(dummy_image_size)
+    mm_data = {"image": [dummy_image] * num_imgs}
+
+    processed_inputs = processor.apply(prompt, mm_data, hf_processor_mm_kwargs)
+
+    # Ensure the placeholders format are correct
+    hf_processor = processor.info.get_hf_processor(**hf_processor_mm_kwargs)
+    hf_processed_inputs = hf_processor(text=prompt, images=mm_data["image"])
+    assert processed_inputs["prompt_token_ids"] == hf_processed_inputs[
+        "input_ids"][0]
+
+    # Ensure we have the right number of placeholders per num_crops size
+    image_token_id = ctx.get_hf_config().image_token_id
+    img_tok_count = processed_inputs["prompt_token_ids"].count(image_token_id)
+    assert img_tok_count == expected_toks_per_img * num_imgs

tests/models/registry.py

@@ -344,6 +344,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "Qwen2_5_VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2.5-VL-3B-Instruct",  # noqa: E501
                                                           min_transformers_version="4.49"),  # noqa: E501
     "SkyworkR1VChatModel": _HfExamplesInfo("Skywork/Skywork-R1V-38B"),
+    "SmolVLMForConditionalGeneration": _HfExamplesInfo("HuggingFaceTB/SmolVLM2-2.2B-Instruct"),  # noqa: E501
     "UltravoxModel": _HfExamplesInfo("fixie-ai/ultravox-v0_5-llama-3_2-1b",  # noqa: E501
                                      trust_remote_code=True,
                                      max_transformers_version="4.50"),

vllm/entrypoints/chat_utils.py

@@ -498,7 +498,7 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
                                               hf_config.image_token_index)
             if model_type in ("aya_vision", "chameleon", "deepseek_vl_v2",
                               "internvl_chat", "skywork_chat", "NVLM_D",
-                              "h2ovl_chat", "idefics3"):
+                              "h2ovl_chat", "idefics3", "smolvlm"):
                 return "<image>"
             if model_type in ("mllama", "llama4"):
                 return "<|image|>"

vllm/model_executor/models/idefics3.py

@@ -206,6 +206,16 @@ class Idefics3ProcessingInfo(BaseProcessingInfo):
 
         return grid_w * grid_h + 1
 
+    def _get_image_token(
+            self,
+            processor: Optional[Idefics3Processor]) -> tuple[str, str, str]:
+        if processor is None:
+            processor = self.get_hf_processor()
+        image_token = processor.image_token.content
+        fake_image_token = processor.fake_image_token.content
+        global_image_token = processor.global_image_tag
+        return image_token, fake_image_token, global_image_token
+
     def get_image_repl(
         self,
         *,
@@ -216,9 +226,8 @@ class Idefics3ProcessingInfo(BaseProcessingInfo):
         if processor is None:
             processor = self.get_hf_processor()
 
-        image_token = processor.image_token.content
-        fake_image_token = processor.fake_image_token.content
-        global_img_token = processor.global_image_tag
+        image_token, fake_image_token, global_img_token = self._get_image_token(
+            processor)
         image_seq_len = processor.image_seq_len
         grid_placeholder = "<row_{n_h}_col_{n_w}>"
 
@@ -300,7 +309,7 @@ class Idefics3DummyInputsBuilder(BaseDummyInputsBuilder[Idefics3ProcessingInfo]
         hf_processor = self.info.get_hf_processor()
         image_processor: Idefics3ImageProcessor = hf_processor.image_processor
         longest_edge = image_processor.max_image_size['longest_edge']
-        image_token = hf_processor.image_token.content
+        image_token, _, _ = self.info._get_image_token(hf_processor)
 
         mm_data = {
             "image":
@@ -382,7 +391,7 @@ class Idefics3MultiModalProcessor(
         out_mm_kwargs: MultiModalKwargs,
     ) -> Sequence[PromptUpdate]:
         hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
-        image_token = hf_processor.image_token.content
+        image_token, _, _ = self.info._get_image_token(hf_processor)
 
         def get_replacement_idefics3(item_idx: int) -> PromptUpdateDetails:
             images = mm_items.get_items("image", ImageProcessorItems)

vllm/model_executor/models/registry.py

@@ -175,6 +175,7 @@ _MULTIMODAL_MODELS = {
     "H2OVLChatModel": ("h2ovl", "H2OVLChatModel"),
     "InternVLChatModel": ("internvl", "InternVLChatModel"),
     "Idefics3ForConditionalGeneration":("idefics3","Idefics3ForConditionalGeneration"),
+    "SmolVLMForConditionalGeneration": ("smolvlm","SmolVLMForConditionalGeneration"),  # noqa: E501
     "LlavaForConditionalGeneration": ("llava", "LlavaForConditionalGeneration"),
     "LlavaNextForConditionalGeneration": ("llava_next", "LlavaNextForConditionalGeneration"),  # noqa: E501
     "LlavaNextVideoForConditionalGeneration": ("llava_next_video", "LlavaNextVideoForConditionalGeneration"),  # noqa: E501

vllm/model_executor/models/smolvlm.py

+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Dict, Optional
+
+from transformers import SmolVLMProcessor
+
+from vllm.config import VllmConfig
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+# yapf: disable
+from .idefics3 import Idefics3DummyInputsBuilder as SmolVLMDummyInputsBuilder
+from .idefics3 import Idefics3ForConditionalGeneration
+from .idefics3 import Idefics3MultiModalProcessor as SmolVLMMultiModalProcessor
+from .idefics3 import Idefics3ProcessingInfo
+
+# yapf: enable
+
+
+class SmolVLMProcessingInfo(Idefics3ProcessingInfo):
+
+    def get_hf_processor(
+        self,
+        *,
+        max_image_size: Optional[Dict[str, int]] = None,
+        **kwargs: object,
+    ) -> SmolVLMProcessor:
+        if max_image_size is not None:
+            kwargs["max_image_size"] = max_image_size
+
+        return self.ctx.get_hf_processor(SmolVLMProcessor, **kwargs)
+
+    def _get_image_token(
+            self, processor: Optional[SmolVLMProcessor]) -> tuple[str, str]:
+        if processor is None:
+            processor = self.get_hf_processor()
+        image_token = processor.image_token
+        fake_image_token = processor.fake_image_token
+        global_image_token = processor.global_image_token
+        return image_token, fake_image_token, global_image_token
+
+
+@MULTIMODAL_REGISTRY.register_processor(SmolVLMMultiModalProcessor,
+                                        info=SmolVLMProcessingInfo,
+                                        dummy_inputs=SmolVLMDummyInputsBuilder)
+class SmolVLMForConditionalGeneration(Idefics3ForConditionalGeneration):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(
+            vllm_config=vllm_config,
+            prefix=prefix,
+        )